Simulation analysis of intermittent arc grounding fault applying with improved cybernetic arc model

Li, Shurong; Yongduan, Xue; Feng, G.; Xu, Bingyin

doi:10.1049/joe.2018.8457

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Furthermore, data extraction from users should be acquired through peers, and users' privacy should be prioritized during this process [55]. In addition, methods should be created to try to capture the context and nature of the players' relationships in a cyberbullying incident, since this is a critical component in identifying deliberate damage and repeated aggressions among peers [56].…”

Section: Discussionmentioning

confidence: 99%

“…The definition of the emotional component of the text is used to study various forms and manifestations of online aggressiveness: trolling [38,45], verbal hostility [39,[46][47][48], cyberbullying through mobile applications [34,41,49], various types of manipulation [50][51][52], inciting discord [53], etc. The network component of such a form of offline aggression as mass protest is the subject of a complex method of cybermetry, or cybermetric analysis [54][55][56]. Cybermetry is used for segmenting information flows based on search queries and marker dictionaries; in total, the study includes dictionaries of markers of 14 types of social media documents according to the degree of the radicalism of protest attitudes and message objects expressed in them.…”

Section: Machine Learningmentioning

confidence: 99%

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A Review of Machine Learning Techniques in Cyberbullying Detection

Sultan¹,

Omarov²,

Kozhamkulova³

et al. 2023

Computers, Materials &Amp; Continua

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Automatic identification of cyberbullying is a problem that is gaining traction, especially in the Machine Learning areas. Not only is it complicated, but it has also become a pressing necessity, considering how social media has become an integral part of adolescents' lives and how serious the impacts of cyberbullying and online harassment can be, particularly among teenagers. This paper contains a systematic literature review of modern strategies, machine learning methods, and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet. We undertake an in-depth review of 13 papers from four scientific databases. The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing. In this review, we consider a cyberbullying detection framework on social media platforms, which includes data collection, data processing, feature selection, feature extraction, and the application of machine learning to classify whether texts contain cyberbullying or not. This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon's description and depiction, allowing future solutions to be more practical and effective.

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Section: Discussionmentioning

confidence: 99%